inference/views.py

from django.http import JsonResponse
from rest_framework.response import Response
from rest_framework.views import APIView
from mistralai import Mistral
import os
import requests
from openai import OpenAI
from ollama import Client
from django.http import FileResponse
import io

class TTSView(APIView):
    def post(self, request, format=None):
        # Define the API endpoint
        # Define the URL for the TTS API
        url = 'http://localhost:5002/api/tts'

        # Define the multiline text
        text = "This is the first line"

        # Prepare the parameters for the GET request
        params = {
            'text': text
        }

        # Make the GET request
        response = requests.get(url, params=params)

        # Check if the request was successful
        if response.status_code == 200:
            # Save the audio response as a WAV file
            # Create a file-like object with the audio data
            audio_data = io.BytesIO(response.content)

            # Return the audio file as a response
            return FileResponse(audio_data, as_attachment=True, filename='audio_output.wav')
        else:
            return Response({"error": "Failed to synthesize speech"}, status=response.status_code)

class SpeechASRView(APIView):
    def post(self, request, format=None):
        try: 
            data = request.data
            ##prompt =  data['prompt']
            audio = data['audio']
            print('hre1')
            client = OpenAI(api_key="cant-be-empty", base_url="http://0.0.0.0:11800/v1/")
            print('her2')
            #filename= '/home/gaganyatri/Music/test1.flac'
            audio_bytes = audio.read()

            #audio_file = open(filename, "rb")

            transcript = client.audio.transcriptions.create(
                model="Systran/faster-distil-whisper-small.en", file=audio_bytes
            )

            #print(transcript.text)
            voice_content = transcript.text
            return Response({"response": voice_content})
        except Exception as e:
            print(f"An error occurred: {e}")
            return Response({'error': 'Something went wrong'}, status=500)


class SpeechToSpeechView(APIView):
    def post(self, request, format=None):
        try: 
            data = request.data
            ##prompt =  data['prompt']
            audio = data['audio']

            client = OpenAI(api_key="cant-be-empty", base_url="http://0.0.0.0:11800/v1/")

            #filename= '/home/gaganyatri/Music/test1.flac'
            audio_bytes = audio.read()

            #audio_file = open(filename, "rb")

            transcript = client.audio.transcriptions.create(
                model="Systran/faster-distil-whisper-small.en", file=audio_bytes
            )

            #print(transcript.text)
            voice_content = transcript.text
                        #content = 'audio recieved'
            system_prompt = "Please summarize the following prompt into a concise and clear statement:"


            model = "mistral-nemo:latest"
            client = Client(host='http://localhost:11434')
            response = client.chat(
            model=model,
            messages=[
                {
                    "role": "system",
                    "content": system_prompt
                },
                {
                    "role": "user",
                    "content": voice_content,
                }
            ],
            )

            # Extract the model's response about the image
            response_text = response['message']['content'].strip()

            url = 'http://localhost:5002/api/tts'

            # Define the multiline text
            #text = "This is the first line"

            # Prepare the parameters for the GET request
            params = {
                'text': response_text
            }

            # Make the GET request
            response = requests.get(url, params=params)

            # Check if the request was successful
            if response.status_code == 200:
                # Save the audio response as a WAV file
                # Create a file-like object with the audio data
                audio_data = io.BytesIO(response.content)

                # Return the audio file as a response
                return FileResponse(audio_data, as_attachment=True, filename='audio_output.wav')
            else:
                return Response({"error": "Failed to synthesize speech"}, status=response.status_code)

        except Exception as e:
            print(f"An error occurred: {e}")
            return Response({'error': 'Something went wrong'}, status=500)

class SpeechLLMView(APIView):
    def post(self, request, format=None):
        try: 
            data = request.data
            ##prompt =  data['prompt']
            audio = data['audio']

            client = OpenAI(api_key="cant-be-empty", base_url="http://localhost:11800/v1/")

            #filename= '/home/gaganyatri/Music/test1.flac'
            audio_bytes = audio.read()

            #audio_file = open(filename, "rb")

            transcript = client.audio.transcriptions.create(
                model="Systran/faster-distil-whisper-small.en", file=audio_bytes
            )

            #print(transcript.text)
            voice_content = transcript.text
                        #content = 'audio recieved'

            model = "mistral-nemo:latest"
            client = Client(host='http://localhost:11434')
            response = client.chat(
            model=model,
            messages=[{
            "role": "user",
            "content": voice_content,
            }],
            )

            # Extract the model's response about the image
            response_text = response['message']['content'].strip()

            return Response({"response": response_text})
        except Exception as e:
            print(f"An error occurred: {e}")
            return Response({'error': 'Something went wrong'}, status=500)

class TranslateLLMView(APIView):
    def post(self, request, format=None):
        try: 
            data = request.data
            prompt =  data['messages'][0]['prompt']
            # Specify model
            source_language = data['sourceLanguage']
            target_language = data['targetLanguage']
            #model = data['model']
            # Define the messages for the chat
            api_key=os.getenv("SARVAM_API_KEY", "")
            url = "https://api.sarvam.ai/translate"

            payload = {
                "input": prompt,
                "source_language_code": source_language,
                "target_language_code": target_language,
                "speaker_gender": "Male",
                "mode": "formal",
                "model": "mayura:v1",
                "enable_preprocessing": True
            }
            headers = {"Content-Type": "application/json",
                    'API-Subscription-Key': f"{api_key}"
                    }

            response = requests.request("POST", url, json=payload, headers=headers)
            content = response.text
            #print(chat_response.choices[0].message.content)
            # Return the content of the response
            return Response({"response": content})
        except Exception as e:
            print(f"An error occurred: {e}")
            return Response({'error': 'Something went wrong'}, status=500)

class TextLLMView(APIView):
    def post(self, request, format=None):
        try:
            data = request.data

            isOnline = data['isOnline']

            prompt =  data['messages'][0]['prompt']
            # Specify model
            #model = "pixtral-12b-2409"
            model = data['model']
            # Define the messages for the chat
            messages = [
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "text",
                            "text": prompt
                        }
                    ]
                }
            ]

            if(isOnline): 
                api_key = os.environ["MISTRAL_API_KEY"]

                # Initialize the Mistral client
                client = Mistral(api_key=api_key)


                # Get the chat response
                chat_response = client.chat.complete(
                    model=model,
                    messages=messages
                )

                content = chat_response.choices[0].message.content
            else:
                content = "helloWorld"

            #print(chat_response.choices[0].message.content)
            # Return the content of the response
            return Response({"response": content})
        except Exception as e:
            print(f"An error occurred: {e}")
            return Response({'error': 'Something went wrong'}, status=500)

class IndicLLMView(APIView):
    def post(self, request, format=None):
        try:
            data = request.data

            isOnline = data['isOnline']

            print(isOnline)
            prompt =  data['messages'][0]['prompt']
            # Specify model
            #model = "pixtral-12b-2409"
            model = data['model']
            # Define the messages for the chat

            client = Client(host='http://localhost:11434')
            response = client.chat(
            model=model,
            messages=[{
            "role": "user",
            "content": prompt,
            }],
            )

            # Extract the model's response about the image
            response_text = response['message']['content'].strip()

            #print(chat_response.choices[0].message.content)
            # Return the content of the response
            return Response({"response": response_text})
        except Exception as e:
            print(f"An error occurred: {e}")
            return Response({'error': 'Something went wrong'}, status=500)

class LlamaVisionView(APIView):
    def post(self, request, format=None):
        try:
            data = request.data

            print("gere")

            image_data = (data['messages'][0]['image'][0])
            prompt =  data['messages'][0]['prompt']
            # Specify model
            #model = "pixtral-12b-2409"
            model = data['model']
            # Define the messages for the chat

            # Define the messages for the chat

            print("gere")
            client = Client(host='http://localhost:21434')
            response = client.chat(
            model="x/llama3.2-vision:latest",
            messages=[{
            "role": "user",
            "content": prompt,
            "images": [image_data]
            }],
            )

            print("gere")
            # Extract the model's response about the image
            response_text = response['message']['content'].strip()

            print(response_text)
            content = response_text
            print("gere")


            #print(chat_response.choices[0].message.content)
            # Return the content of the response
            return Response({"response": content})
        except Exception as e:
            print(f"An error occurred: {e}")
            return Response({'error': 'Something went wrong'}, status=500)


class VisionLLMView(APIView):
    def post(self, request, format=None):
        try:
            data = request.data
            api_key = os.environ["MISTRAL_API_KEY"]

            # Initialize the Mistral client
            client = Mistral(api_key=api_key)

            image_data = (data['messages'][0]['image'][0])
            prompt =  data['messages'][0]['prompt']
            # Specify model
            #model = "pixtral-12b-2409"
            model = data['model']
            # Define the messages for the chat
            messages = [
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "text",
                            "text": prompt
                        },
                        {
                            "type": "image_url",
                            "image_url": f"data:image/jpeg;base64,{image_data}" 
                        }
                    ]
                }
            ]

            # Get the chat response
            chat_response = client.chat.complete(
                model=model,
                messages=messages
            )

            content = chat_response.choices[0].message.content
            #print(chat_response.choices[0].message.content)
            # Return the content of the response
            return Response({"response": content})
        except Exception as e:
            print(f"An error occurred: {e}")
            return Response({'error': 'Something went wrong'}, status=500)


class NIMVisionLLMView(APIView):
    def post(self, request, format=None):
        try:
            invoke_url = "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-11b-vision-instruct/chat/completions"
            stream = False
            api_key = os.environ["NIM_API_KEY"]
            data = request.data
            model = data['model']
            print(model)
            image_data = (data['messages'][0]['image'][0])
            prompt =  data['messages'][0]['prompt']
            headers = {
            "Authorization": f"Bearer {api_key}",
            "Accept": "text/event-stream" if stream else "application/json"
            }
            payload = {
            "model": model,
            "messages": [
                {
                "role": "user",
                "content": f'{prompt} <img src="data:image/png;base64,{image_data}" />'
                }
            ],
            "max_tokens": 512,
            "temperature": 1.00,
            "top_p": 1.00,
            "stream": stream
            }
            response = requests.post(invoke_url, headers=headers, json=payload)

            if stream:
                for line in response.iter_lines():
                    if line:
                        #print(line.decode("utf-8"))
                        data = line.decode("utf-8")
                        #content = json.loads(data)['choices'][0]['delta'].get('content', '') 
            else:
                #print(response.json())
                data =  response.json()
                content = data['choices'][0]['message']['content']

                #print(content)
                return Response({"response": content})


        except Exception as e:  # Added general exception handling
            print(f"An error occurred: {e}")
            return Response({'error': 'Something went wrong'}, status=500)